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Building AI agents in 2026 means choosing from a growing collection of frameworks. Three names come up in almost every conversation: LangGraph, CrewAI, and AutoGen. Each takes a different approach to the same problem, how to orchestrate multiple AI calls into something that acts like an agent.
AI agents have moved from research labs to practical tools that anyone can use. Unlike simple chatbots that answer questions, AI agents take action. They can research topics, write drafts, schedule meetings, manage email, and coordinate complex multi-step workflows.
Building an AI agent used to require a team of engineers and months of work. In 2026, a single developer can build a functional agent in a weekend. The frameworks, tools, and cloud services available today abstract away most of the complexity.
The AI coding agent space exploded in 2026. What started as autocomplete has evolved into full agents that can plan, write, test, and deploy code across entire codebases. Four names dominate the conversation right now: Claude Code, OpenAI Codex, Pi by Pi.ai, and Hermes by Nous Research. Each takes a
Security teams have long worried about AI-powered attacks. That worry is now a reality. In early 2026, multiple security firms reported finding autonomous AI agents actively conducting hacking campaigns against real targets. These are not hypothetical threats. They are happening right now.
Model Context Protocol, or MCP, has become the standard way to connect AI agents to external tools and data sources. Anthropic created it, and the industry adopted it fast. By mid-2026, thousands of MCP servers exist, connecting AI agents to databases, APIs, file systems, and internal tools.
AI agents are great at working alone. Give one a task, and it plans, executes, and delivers. But the real power of AI agents emerges when they work together. That requires communication. Enter the Agent-to-Agent protocol, commonly called A2A.
Model Context Protocol changed how AI agents access tools and data. Now an evolution of that idea, called Stateless MCP, promises to solve one of the biggest headaches in multi-agent systems: managing shared state between agents.
Uber runs one of the largest enterprise AI deployments in the world. Thousands of AI agents handle everything from customer support to fraud detection to route optimization. Each agent is a potential attack vector. Uber's security team published details on how they manage that risk.
